Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
6-2019
Abstract
Biclustering of observations and the variables is of interest in many scientific disciplines. In a single set of data matrix it is handled through the singular value decomposition. Here we deal with two sets of variables: response and predictor sets. We model the joint relationship via regression models and then apply SVD on the coefficient matrix. The sparseness condition is introduced via Group Lasso. The approach discussed here is quite general and is illustrated with an example from Finance.
Keywords
multivariate regression, singular value decomposition, dimension reduction, mixture models
Discipline
Finance and Financial Management
Research Areas
Finance; Quantitative Finance
Publication
Proceedings of the 19th International Conference, Faro, Portugal, 2019 June 12-14
First Page
533
Last Page
549
ISBN
9783030227401
Identifier
10.1007/978-3-030-22741-8_38
Publisher
Springer Verlag
City or Country
Faro, Portugal
Citation
VELU, Raja; ZHOU, Zhaoque; and TEE, Chyng Wen.
Biclustering via mixtures of regression models. (2019). Proceedings of the 19th International Conference, Faro, Portugal, 2019 June 12-14. 533-549.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/6405
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-3-030-22741-8_38